Part 2: Automation to Detect and Stop Fraudulent Transactions

Picking up where we left off last time, welcome to Part 2 of the recap of the BAS Fraud Panel with Informed’s Director of Auto Lending Strategy, Jessica Gonzalez, Kevin Faragher, Senior Director of Product and Strategy at Ally Financial and Michael Reynolds, Business Technology Senior Manager for Service Digitization at KeyBank, moderated by Whitney McDonald, Deputy Editor Of  Bank Automation News. 

During the past week, we’ve seen several more news articles about fraud, so we know this topic is timely and that the panelist’s tips are needed.

Whitney – I think it’d be beneficial for people to have Kevin and Michael share what your banks are doing to fight fraud.

Michael – Let’s say you get 10,000 new fraudulent accounts opened… The reporting requirements for both local law enforcement, the FBI, whichever other kind of lines of business division oversight that you have, you now have 10,000 cases to prepare and probably no one in the organization to do that in the in the short term because we all know the faster that you can react to a situation like that, the more likely that people will be held accountable for those actions. 

So from an automation perspective, we’re looking at how do we pull the necessary data, typically screenshots, reports, the slivers of information from a document to support our alleged crime, and then to facilitate that you have to enter these activities into a certain form. Whether it’s mine or, the federal bureau, they have a format that they like. So, from a law perspective, we’ll automate those andhave the case files ready and typically people review and sign off on them and then filter them to the correct agencies. Kevin, kind of interested in what you have.

Kevin – One thing about indirect lending, we always have something we can take back  – like a car.

Interestingly enough, you would think that we as a lender,  could turn on OnStar and find our car just going remote. But there are rules and regulations, privacy, so you can’t do that. So it’s really important. Is it somebody that commits fraud and they actually pay you or do you catch them?

And do you care if you catch them? You probably do because you want the information so you can cycle forward. So a lot of what we’re doing is learning where we can move that into our front end system so that when we get those credit applications and contracts from our indirect partners, we’re better able to spot them and then learning more by looking at the documents. I think I read an article that said you Google free fake fraud documents. Google has like 40,000 different versions. So there’s no way that humans can deal with that. So it’s like that’s where we keep getting burned. We’re moving information forward and trying to be better about stopping it before it actually turns into a receivable. 

Jessica – To add to that,  what we do is, we really make sure that we’re scouring the dark we – Getting all of the template sites documented. We’re looking for them consistently, adding to our database. We’re always doing enhancements, making sure that we have all of those fraudulent templates and keeping up with the ever changing dynamic trends. We also look for missing data that visually cues fraudulent data within calculations. Wey have a really low false positive rate – our false positive rate is .02. What that means to you is that when we tell you that it’s fraud, it is very likely fraud. We’re not going to say that it is true fraud because we’ve let lenders and banks make those decisions. But you have all the data that’s extracted, so that if you want to automate your FCRA process, if you want to automate some of the regulatory compliance issues that you have to do for a reporting agency, you’re able to do it and you  don’t have to waste valuable man hours. You’re able to say that this is true fraud. You don’t have to go back and question your fraud team. With informed you can really just say it is fraud, I know it’s fraud, and then start aggregating that data as a true fraud flag. You can look across your portfolio see what other fraud indicators you see, without bias and letting the data speak for itself.

Whitney – Kevin, can you share how Ally handles notifying on potential fraud? This is something that you were exploring.

Kevin – One situation is where we take the example of a person who committed a crime but is actually making the payments. Sometimes the person who they stole the ID from, if they did steal an ID, goes to get a mortgage or something and they get told they can’t because they have too much debt outstanding. And also, they say, “I didn’t buy an Escalade,” so they’ll contact us and then we’ll go through a process of ensuring we can help that customer get that trade line taken care of. But if we discover fraud ourselves then we’ll proactively reach out to the customer who we think has been compromised, let them know, and start the process. You gotta get documentation in place and then you can go into what is most important – helping the person who was compromised to get their credit history restored because that is extremely important. 

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